{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so,divide the values by 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images/255.0\n",
    "test_images = test_images/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i],cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set up the layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(128,activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 6s 101us/sample - loss: 0.4991 - accuracy: 0.8253\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 5s 76us/sample - loss: 0.3812 - accuracy: 0.8631\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 5s 88us/sample - loss: 0.3411 - accuracy: 0.8759\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 5s 87us/sample - loss: 0.3168 - accuracy: 0.8841\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 6s 94us/sample - loss: 0.2984 - accuracy: 0.8906\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 6s 92us/sample - loss: 0.2855 - accuracy: 0.8936\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 5s 78us/sample - loss: 0.2730 - accuracy: 0.8993\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 5s 79us/sample - loss: 0.2609 - accuracy: 0.9032\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 5s 80us/sample - loss: 0.2510 - accuracy: 0.9057\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 6s 92us/sample - loss: 0.2413 - accuracy: 0.9104\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2b1a9685b48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images,train_labels,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 - 0s - loss: 0.3402 - accuracy: 0.8813\n",
      "\n",
      "Test accuracy: 0.8813\n"
     ]
    }
   ],
   "source": [
    "test_loss,test_acc = model.evaluate(test_images,test_labels,verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model,tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.8909691e-08, 9.3273711e-10, 4.3977479e-08, 7.1095151e-11,\n",
       "       1.2535948e-08, 1.9069314e-03, 1.3237249e-07, 1.0304061e-02,\n",
       "       3.2987171e-07, 9.8778850e-01], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATEklEQVR4nO3dfbBdVXnH8e9KQsgrEMI7iVy1SUEBA2JGRRkVpIJOlFYHqXaKVNsZfG8VaKcN9GWmotZqp1VEwGoFrI1hRNsSQAV5DRAEEt6rJJgEAgHJG6+Bp3/sHb25e+1798m9yYLk+5m5wznPWevsfc4Nv7vPWmvvkyICSdK2N6r0DkjSjsoAlqRCDGBJKsQAlqRCDGBJKsQAlqRCxpTeAam0PfbYI/r6+krvhrZTixYtWh0Re+YeM4C1w+vr6+OWW24pvRvaTqWUlrU95hCEJGXssw+k1P1nn31634YBLEkZq1Zt3fZgAEtSMT2NATtZoa1p6dKlrF69OpXeD2lb6SmAnazQ1nTEEUeU3gVpm3IIQpIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKGVN6BzRynn/++UZt1Kj839iUUufnfeaZZxq1nXfeOdv2/vvvb9RmzJjReVvSjsQjYEkqxACWpEIMYEkqxACWpEIMYEkqxFUQIyQiOtUgvzJhxYoV2bY33HBDo3bcccdl206cOHGwXdxibSsecubPn9+onX766SO5O9J2wyNgSSrEAJakQgxgSSrEAJakQpyE24raTgPOueaaa7L1hQsXNmorV67Mtv3EJz7ReXu9eOSRRxq1BQsWZNtOnjx5q+yDtD3yCFiSCjGAJakQA1iSCjGAJakQA1iSCnEVxAjJXQx9zJj823vzzTc3anfffXe27d57792o5S56DnDCCSc0alOmTMm2ffrppxu1Aw44INv2sccea9TWrl2bbbv//vtn65KaPAKWpEIMYEkqxACWpEIMYEkqxEm4LfDCCy80arkJtw0bNmT7z5s3r1Fru+ZubrJs3bp12ba9XJM4V7/zzjuzbadNm9aotU3u5SYjJeV5BCxJhRjAklSIASxJhRjAklSIASxJhbzkVkHkZu9TStm2udUKbW1z9bYZ/dGjRw+2i79xzjnnZOu504vHjRuXbbts2bJGLbcyou15N27cmG2be71t36qcW6GxZs2abNtnnnmmUWtbDbK1vsVZeqnwCFiSCjGAJakQA1iSCjGAJamQF8UkXC8Ta231nF6+lTg34dZ1sg3g4osvbtQefvjhbNvDDjusUWubLHviiScatd133z3bdurUqY3a6tWrs23Xr1/feR9y2k5xfvLJJxu1tusXz5o1q/P2pO2RR8CSVIgBLEmFGMCSVIgBLEmFvCgm4XqZWMud3ZarQX4SrW1bvUy4XXDBBY3afffd16hNnz492z/3JZdtk1pPPfVUo9b2xZe56wS3vd4JEyY0am1n2PUySZqzYMGCbN1JOO3oPAKWpEIMYEkqxACWpEIMYEkqxACWpEK22iqItpUJObkZ9bZVAbnTi3s55bjNypUrG7X58+dn2+ZWJsyYMaNRy53uC/lr5uZWRgDstNNOjVrbCoTcacBtcu9Z2zcz59q2Xcs3t2/XXXdd5/2SdiQeAUtSIQawJBViAEtSIQawJBXS8yTcwOvmtp3CO9yJsV5OdX300Uez9aVLlzZq9957b7btQw891KiNHTs223aXXXZp1HLX7V27dm22/3PPPdeo5SbmIP/+5l4X5K/nu9tuu2Xb5l5b25eQ5iZEx48fn22be45JkyZl2y5ZsmSz+7nJTWl75hGwJBViAEtSIQawJBViAEtSIQawJBXS8yqIrhcuX7VqVaO2bNmybNsNGzZ0qkF+pvyBBx7Its2dmjtmTP4lT548uVFrO516zZo1nfarbVu5/WpbVZA7PfjZZ5/Ntt13330btbaVGLl9mDJlSrZt7pTqxx9/PNs2t+Kh7duhBz5H2yoMaXvlEbAkFWIAS1IhBrAkFWIAS1Ihw74e8JVXXpmt566v2zYplTuVuG1CJjcJ2MvEWts1enMTRW3XJM6dNpybwGqbxMvtQ9vrzV13t+3U3txpx22nafci99raTjXPTUa2TRq2/d6kHYVHwJJUiAEsSYUYwJJUiAEsSYUYwJJUSE/T0GvXruXyyy/frHb++edn2x544IGNWu5UWejtNODhXkg8ty3Iz9S3zfSvW7eu07baLjCeu9h822vIrc7IneYNcNdddzVqbSsQejntN7fqou1U8XHjxnXqD7DXXnttdj/3DdDS9swjYEkqxACWpEIMYEkqxACWpEJ6moSbOHEis2fP3qx24403ZtsuXry4Ubv22ms7b6ttQiY3ibb77rtn2+bqu+66a7ZtbrKq7VTkxx57rFHLfdty7pq7kL9Gb9u3QN9+++2N2qGHHppt29fX16hdccUV2ba506l7+SbrttOI99tvv0Yt9y3S0JzM9HrA2tF4BCxJhRjAklSIASxJhRjAklSIASxJhfS0CmL06NGNi37PnTu3c/+2i6EvXLiwUcutKgC4/vrrG7WlS5dm295xxx2NWtsptLkVD20rE3KrBXIrLg455JBs/2OOOaZRO/7447Ntc6f29mLOnDnZ+oMPPtioTZ06Nds2t4qh7ZTu3OqI3Dc7A8ycOXOz+8N9rdJLjUfAklSIASxJhRjAklSIASxJhWzTr6Vtuy7s0Ucf3akGcOqpp47oPm3vLr300tK70Fkvp0JL2wP/xUtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBUyppfGixYtWp1SWra1dkY7vANK74C0LfUUwBGx59baEUna0TgEIUmFGMCSVEhPQxCSVMoZZ5zRue3nPve5rbgnI2dEAjglTgDmAwdFcE+H9kuBIyJYPaC+PoJJPWy3p/aDPM/JwOURrMw89hrgHGASsBT4QARrU2Is8HXgCOAF4JMRXJUSOwM/AKYBX43gq/XznAt8LYKft+zDe4BDI/i7frXbgbsiOKnjazgigo8NqJ8FrI/gi0M9x5a0H+R5+oA3RnBRff8Q4C8iOHk4z6sXj+0xELe1kToCPgm4Fng/cNYIPee2dDKwBJoBDJwHfCaCq1PiFOCzwN8AHwGI4JCU2Av435R4HfB7wCLgeOBW4Kt1iI9qC9/aacCcTXdS4iCqIaKjUmJiBBuG+Rq3tT7gD6EK4AgWp8S0lHhZBA8W3bMBtnB1zx6w+QGE/dr7nX322dt0m9t6e5uklG3fvronIob1AzEJYgXETIh7+tXfAnEVxDyIeyAuhEj1Y0sh9oAYD3EZxEfq+vp+/T8LcTPEHRB/27Lt9RD/BHErxI8h9qzrsyBurPteAjGlrQ7x3vp57oW4DWL8gG2s7bff0yHuqm//G8QH+7X7McRsiOMgvgQxFuK2+rFLIfYb5D2cCfHTAbW/hzgN4psQJ/WrXwVxNsRNEPdBvLmunwzxr/Xtd0LcUL/HZ0F8pq6/sn6/F0FcA3FgZl/OgvgPiJ9A3N/vd5MgvgCxBGIxxIlD1G+EWFO/p5+ua5+EOG24/+ZeDD/ALfYbuX4vpX0dzmsc+DMSk3DvAS6L4D7g8ZQ4vN9jhwGfAl4FvAI4st9jk4AfAhdF8I3+T5gSxwIzgNnALOC1KXFUZtsTgVsjOBy4Gjizrn8bOD2CQ4HFg9UjmAfcQjW0MCuCpwZsYwm/PTJ9HzC9vn078O6UGJMSLwdeWz92BbAPsBD4fErMARZFZnijnyOpjpb7OxH4T+BiaAxBjIlgNtV7e2b/B+rhoDOA4yMaf6XPBT4ewWuBz0A1PJJxKPBO4A3A3JTYD/h9qt/Fa4BjgC+kxL6D1M8Arqnf03+un/cW4M2DvA/SDmUkhiBOAr5c3/5ufX9TmNwUwXKAlLiN6mPptfVjPwA+H8GFmec8tv7Z9JF9ElUg/2xAuxeoQgrgO8D8lNgV2C2Cq+v6t4D/aqt3eH2nAP+SEnOBS4Fn6/oFwEFUobIMuB7YGMFGqo/epMROwAJgTkp8CXgZ8O0ILh2wjX2BRzfdqYcyHo1gWUosBy5IiSkR/LpuMr/+7yKq93STt1KNSR8bwdr+G0iJScAb6/dik51bXvMP6j9ET6XET6n+EL4JuDiC54FVKXE18LpB6mszz/sIsF/LNqUdzrACOCWmAm8DDk6JAEYDkRKn1U2e6df8+QHbuw44LiUuiiAGPjXwjxF8vcddGvg8wxbVpOKxACkxk+rIkDpoP72pXUpcD9w/oPupVEH/BqrgPhG4ARoB/BSwa7/7JwEH1pOVALsAf0A1Hg2/fV8Hvqe/pPqkMZPqD0N/o4AnIpg12OutDXwfg+p3ktNWzxkHjU8YL1Xn2m9E+5XYZonXuJnhDkG8l+qI7oAI+iKYDjxAdVQ0lLnAY+Q/Bi8ATqmP2kiJ/euJroFG1fsA1VHntRGsAX6d0m8+6v4RcHVbvb69Dpic28lN202JUcBfU62IICUmpMTE+vbbqY5+7+rXbwrwLqphjwlUR+tBFUID3Q38Tr/tvI9qRURfBH3Au2kOQ+QsoxoS+HZKvLr/A/UR8QMp8b56O6meHMx5d0qMq//AvgW4merTx4kpMTol9gSOAm4apJ57T2dSDem85EXEFv1PaL8XzzZLvMaBhhvAJwGXDKh9n/ojeAefAsalxOf7FyO4nGr2/IaUWAzMIx+QG4BXp8QiqiPxTUu4/phqLPIOqvHJoer/DpyTErelxPiBrzEl7gPuoVol8c26vhdwa0rcDZxOFej9zQX+oT66X0A1NLAYNh/vrv0MOCwlElWArYhgxYDHX1WPrQ4qgnuBD1ANNbxywMMfAP6kXt52J1Ww59wE/DdwI/D39fj1JcAdVGPfPwFOi+DhQep3ABtT4vaUfvNJ4a3180oC6tl9lZYSXwF+GMGVpfdla6jXR18NvKkevnlJSim9A/gK1XDbeRHRaYFrSukCqk9Ej0TEwT1sbzrVp6h9qD5FnRsRX+nQbxzVH+6dqYap5kXEmYP32qz/aKphrBUR8a6OfZZSffJ5HtgYEUd07Lcb1fDawVSfEk+JiBuG6PO7/Hb+B6qht7kR8eWWLv37fhr4cL2txcCHIuLpDv0+SbX8NAHf6LKtIY3Ucgp/hvcDsTfEnNL7sRVf3wyIt5Tej+G9BkYDv6D6n30s1VH/qzr2PQo4HFjS4zb3BQ6vb08G7uuyzTokJtW3d6JalfP6Hrb751SfQn/UQ5+lwB5b8L5+C/hwfXsssNsW/F4eBg7o0HZ/qmHS8fX97wEnd+h3MNXw2QSqP2hXAjOG+2/Ka0G8SESwKpqrI7YbEdwfwVWl92OYZgP/FxG/jIhnqVb9tA3jbCYifgY83usGI+KhiLi1vr2Oar5g/w79IiLW13d3qn86fdxNKU2jmmw+b6i2w5VS2oXqj9P5ABHxbEQ80ePTHA38IiK6nkwzBhifUhpDFaiDLRHd5CDgxoh4MiI2Un2aO6HH/WwwgKXu9gd+1e/+cjqE4UhJKfVRra1f2LH96JTSbVTL/66IiE79qJaVnkY15NGLAC5PKS1KKf1pxz6voFqC+c2U0s9TSuellCb2uN33U62XH3oHI1YAXwQeBB4C1kTE5R26LgGOSilNTSlNoDrTdfoQfYZkAEvd5ZbcbZNJlJTSJKoJ7k9FRG6NdUNEPB8Rs6iuSzI7pTTk2HNKadM49aIt2M0jI+Jw4Djgoyml3MlTA42hGpr5WkQcRjWx3vkiEymlsVQnSnVZ009KaQrVp5aXU61Jn5hS+uBQ/SLibuBsqhOtLqMafhr2XIYBLHW3nM2PeqbR7ePrsKSUdqIK3wsjYv5Q7QeqP9JfBbyjQ/MjgTn1hNp3gbellL7TcTsr6/8+QrU6ZnaHbsuB5f2OzufBZmfTDuU44NaIWNWx/THAAxHxaEQ8R3VS0xu7dIyI8yPi8Ig4imo4aeC6/54ZwFJ3NwMzUkovr4+83k/zpJoRlVJKVOOjd0fEl3rot2e9uoCU0niq4BnySoUR8ZcRMS0i+qhe308iYsgjxJTSxJTS5E23qU5eGnLNd0Q8DPyqXtUA1XjuXYN0GegkOg4/1B4EXp9SmlC/t0dTjasPKaVUnxOQXka13r6X7WZ5PWCpo4jYmFL6GNW67tHABRFxZ5e+KaWLqU5q2SOltBw4MyLO79D1SKo15ovr8VyAv4qI/xmi377At+rlZKOA70XEj7rs6xbaG7ikyjTGABdFxGUd+34cuLD+o/ZL4ENdOtVjsW8H/qzrTkbEwpTSPKrLJWykutxB1xMrvp9Smgo8B3w0In49VIehuA5YkgpxCEKSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJamQ/wefpk2uDSHCVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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